Anatomical segmentation generally refers to the delineation of anatomical structures apparent in medical imaging data. For example, segmentation may be applied by software to medical resonance imaging (MRI) data of a human brain to delineate neuroanatomical structures using boundaries defined by contrast variations within an image set. The same concept may be applied to imaging data from other imaging modalities such as computed tomography (CT), positron emission tomography (PET), or other medical imaging modalities, and applied to other organs, such as the heart, liver, kidneys, or lungs. The segmentation process may be automated through the use of computer software to automatically identify anatomical boundaries. Anatomical segmentation may rely on a number of image features, including signal intensity, global as well as local position with relation to neighboring structures, texture, and shape. Joint information from several different imaging techniques is often combined to provide additional information.
Moreover, segmentation may be used automatically identify specific anatomical features within segment boundaries. Automated segmentation labeling may be useful for efficiently and reliably identifying abnormalities. For example, such abnormalities may be relevant to identifying and/or diagnosing disease and/or pathologies that may affect patient care.
Generally, automated segmentation labeling algorithms rely on registering imaging data to an atlas. The atlas, for example, may be a predefined map of the target organ, such as a brain, heart, kidney, liver, lungs, or other organs, as well as information about the expected characteristics about the target organ. However, anatomic variations among a target population may impede accurate anatomical segmentation that relies on a single reference atlas. For example, morphometric differences may be associated with natural genetic differences, age, gender, ethnicity, and various diseases.
Automatic segmentation techniques may distinguish common underlying anatomical structure present in most organisms. There is often an expected identifiable local and sometimes global arrangement of tissues that can be grouped into structures, or organs. For example, techniques may take advantage of global structure by using landmark locations of easily identifiable locations as the starting point, including graph cuts, machine learning, Bayesian classification, or level sets. Atlas-based segmentation techniques may use global statistical information to distinguish anatomical variations, as opposed to relying on landmarks. Segmentation may then include fitting of the image data to a statistical atlas and classifying the fitted data set in the atlas space.